Mock sample for your project: Amazon S3 on Outposts API

Security Token Service Security Token Service (STS) enables you to request temporary, limited-privilege credentials for Identity and Access Management (IAM) users or for users that you authenticate (federated users). This guide provides descriptions of the STS API. For more information about using this service, see Temporary Security Credentials.

AWS CloudHSM Service This is documentation for AWS CloudHSM Classic. For more information, see AWS CloudHSM Classic FAQs, the AWS CloudHSM Classic User Guide, and the AWS CloudHSM Classic API Reference. For information about the current version of AWS CloudHSM, see AWS CloudHSM, the AWS CloudHSM User Guide, and the AWS CloudHSM API Reference.

IoT Greengrass brings local compute, messaging, data management, sync, and ML inference capabilities to edge devices. This enables devices to collect and analyze data closer to the source of information, react autonomously to local events, and communicate securely with each other on local networks. Local devices can also communicate securely with Amazon Web Services IoT Core and export IoT data to the Amazon Web Services Cloud. IoT Greengrass developers can use Lambda functions and components to create and deploy applications to fleets of edge devices for local operation. IoT Greengrass Version 2 provides a new major version of the IoT Greengrass Core software, new APIs, and a new console. Use this API reference to learn how to use the IoT Greengrass V2 API operations to manage components, manage deployments, and core devices. For more information, see What is IoT Greengrass? in the IoT Greengrass V2 Developer Guide.

AWS Data Pipeline configures and manages a data-driven workflow called a pipeline. AWS Data Pipeline handles the details of scheduling and ensuring that data dependencies are met so that your application can focus on processing the data. AWS Data Pipeline provides a JAR implementation of a task runner called AWS Data Pipeline Task Runner. AWS Data Pipeline Task Runner provides logic for common data management scenarios, such as performing database queries and running data analysis using Amazon Elastic MapReduce (Amazon EMR). You can use AWS Data Pipeline Task Runner as your task runner, or you can write your own task runner to provide custom data management. AWS Data Pipeline implements two main sets of functionality. Use the first set to create a pipeline and define data sources, schedules, dependencies, and the transforms to be performed on the data. Use the second set in your task runner application to receive the next task ready for processing. The logic for performing the task, such as querying the data, running data analysis, or converting the data from one format to another, is contained within the task runner. The task runner performs the task assigned to it by the web service, reporting progress to the web service as it does so. When the task is done, the task runner reports the final success or failure of the task to the web service.

AWS CodeCommit This is the AWS CodeCommit API Reference. This reference provides descriptions of the operations and data types for AWS CodeCommit API along with usage examples. You can use the AWS CodeCommit API to work with the following objects: Repositories, by calling the following: BatchGetRepositories, which returns information about one or more repositories associated with your AWS account. CreateRepository, which creates an AWS CodeCommit repository. DeleteRepository, which deletes an AWS CodeCommit repository. GetRepository, which returns information about a specified repository. ListRepositories, which lists all AWS CodeCommit repositories associated with your AWS account. UpdateRepositoryDescription, which sets or updates the description of the repository. UpdateRepositoryName, which changes the name of the repository. If you change the name of a repository, no other users of that repository can access it until you send them the new HTTPS or SSH URL to use. Branches, by calling the following: CreateBranch, which creates a branch in a specified repository. DeleteBranch, which deletes the specified branch in a repository unless it is the default branch. GetBranch, which returns information about a specified branch. ListBranches, which lists all branches for a specified repository. UpdateDefaultBranch, which changes the default branch for a repository. Files, by calling the following: DeleteFile, which deletes the content of a specified file from a specified branch. GetBlob, which returns the base-64 encoded content of an individual Git blob object in a repository. GetFile, which returns the base-64 encoded content of a specified file. GetFolder, which returns the contents of a specified folder or directory. PutFile, which adds or modifies a single file in a specified repository and branch. Commits, by calling the following: BatchGetCommits, which returns information about one or more commits in a repository. CreateCommit, which creates a commit for changes to a repository. GetCommit, which returns information about a commit, including commit messages and author and committer information. GetDifferences, which returns information about the differences in a valid commit specifier (such as a branch, tag, HEAD, commit ID, or other fully qualified reference). Merges, by calling the following: BatchDescribeMergeConflicts, which returns information about conflicts in a merge between commits in a repository. CreateUnreferencedMergeCommit, which creates an unreferenced commit between two branches or commits for the purpose of comparing them and identifying any potential conflicts. DescribeMergeConflicts, which returns information about merge conflicts between the base, source, and destination versions of a file in a potential merge. GetMergeCommit, which returns information about the merge between a source and destination commit. GetMergeConflicts, which returns information about merge conflicts between the source and destination branch in a pull request. GetMergeOptions, which returns information about the available merge options between two branches or commit specifiers. MergeBranchesByFastForward, which merges two branches using the fast-forward merge option. MergeBranchesBySquash, which merges two branches using the squash merge option. MergeBranchesByThreeWay, which merges two branches using the three-way merge option. Pull requests, by calling the following: CreatePullRequest, which creates a pull request in a specified repository. CreatePullRequestApprovalRule, which creates an approval rule for a specified pull request. DeletePullRequestApprovalRule, which deletes an approval rule for a specified pull request. DescribePullRequestEvents, which returns information about one or more pull request events. EvaluatePullRequestApprovalRules, which evaluates whether a pull request has met all the conditions specified in its associated approval rules. GetCommentsForPullRequest, which returns information about comments on a specified pull request. GetPullRequest, which returns information about a specified pull request. GetPullRequestApprovalStates, which returns information about the approval states for a specified pull request. GetPullRequestOverrideState, which returns information about whether approval rules have been set aside (overriden) for a pull request, and if so, the Amazon Resource Name (ARN) of the user or identity that overrode the rules and their requirements for the pull request. ListPullRequests, which lists all pull requests for a repository. MergePullRequestByFastForward, which merges the source destination branch of a pull request into the specified destination branch for that pull request using the fast-forward merge option. MergePullRequestBySquash, which merges the source destination branch of a pull request into the specified destination branch for that pull request using the squash merge option. MergePullRequestByThreeWay. which merges the source destination branch of a pull request into the specified destination branch for that pull request using the three-way merge option. OverridePullRequestApprovalRules, which sets aside all approval rule requirements for a pull request. PostCommentForPullRequest, which posts a comment to a pull request at the specified line, file, or request. UpdatePullRequestApprovalRuleContent, which updates the structure of an approval rule for a pull request. UpdatePullRequestApprovalState, which updates the state of an approval on a pull request. UpdatePullRequestDescription, which updates the description of a pull request. UpdatePullRequestStatus, which updates the status of a pull request. UpdatePullRequestTitle, which updates the title of a pull request. Approval rule templates, by calling the following: AssociateApprovalRuleTemplateWithRepository, which associates a template with a specified repository. After the template is associated with a repository, AWS CodeCommit creates approval rules that match the template conditions on every pull request created in the specified repository. BatchAssociateApprovalRuleTemplateWithRepositories, which associates a template with one or more specified repositories. After the template is associated with a repository, AWS CodeCommit creates approval rules that match the template conditions on every pull request created in the specified repositories. BatchDisassociateApprovalRuleTemplateFromRepositories, which removes the association between a template and specified repositories so that approval rules based on the template are not automatically created when pull requests are created in those repositories. CreateApprovalRuleTemplate, which creates a template for approval rules that can then be associated with one or more repositories in your AWS account. DeleteApprovalRuleTemplate, which deletes the specified template. It does not remove approval rules on pull requests already created with the template. DisassociateApprovalRuleTemplateFromRepository, which removes the association between a template and a repository so that approval rules based on the template are not automatically created when pull requests are created in the specified repository. GetApprovalRuleTemplate, which returns information about an approval rule template. ListApprovalRuleTemplates, which lists all approval rule templates in the AWS Region in your AWS account. ListAssociatedApprovalRuleTemplatesForRepository, which lists all approval rule templates that are associated with a specified repository. ListRepositoriesForApprovalRuleTemplate, which lists all repositories associated with the specified approval rule template. UpdateApprovalRuleTemplateDescription, which updates the description of an approval rule template. UpdateApprovalRuleTemplateName, which updates the name of an approval rule template. UpdateApprovalRuleTemplateContent, which updates the content of an approval rule template. Comments in a repository, by calling the following: DeleteCommentContent, which deletes the content of a comment on a commit in a repository. GetComment, which returns information about a comment on a commit. GetCommentReactions, which returns information about emoji reactions to comments. GetCommentsForComparedCommit, which returns information about comments on the comparison between two commit specifiers in a repository. PostCommentForComparedCommit, which creates a comment on the comparison between two commit specifiers in a repository. PostCommentReply, which creates a reply to a comment. PutCommentReaction, which creates or updates an emoji reaction to a comment. UpdateComment, which updates the content of a comment on a commit in a repository. Tags used to tag resources in AWS CodeCommit (not Git tags), by calling the following: ListTagsForResource, which gets information about AWS tags for a specified Amazon Resource Name (ARN) in AWS CodeCommit. TagResource, which adds or updates tags for a resource in AWS CodeCommit. UntagResource, which removes tags for a resource in AWS CodeCommit. Triggers, by calling the following: GetRepositoryTriggers, which returns information about triggers configured for a repository. PutRepositoryTriggers, which replaces all triggers for a repository and can be used to create or delete triggers. TestRepositoryTriggers, which tests the functionality of a repository trigger by sending data to the trigger target. For information about how to use AWS CodeCommit, see the AWS CodeCommit User Guide.

Amazon Honeycode is a fully managed service that allows you to quickly build mobile and web apps for teams—without programming. Build Honeycode apps for managing almost anything, like projects, customers, operations, approvals, resources, and even your team.

AWS Application Discovery Service AWS Application Discovery Service helps you plan application migration projects. It automatically identifies servers, virtual machines (VMs), and network dependencies in your on-premises data centers. For more information, see the AWS Application Discovery Service FAQ. Application Discovery Service offers three ways of performing discovery and collecting data about your on-premises servers: Agentless discovery is recommended for environments that use VMware vCenter Server. This mode doesn't require you to install an agent on each host. It does not work in non-VMware environments. Agentless discovery gathers server information regardless of the operating systems, which minimizes the time required for initial on-premises infrastructure assessment. Agentless discovery doesn't collect information about network dependencies, only agent-based discovery collects that information. Agent-based discovery collects a richer set of data than agentless discovery by using the AWS Application Discovery Agent, which you install on one or more hosts in your data center. The agent captures infrastructure and application information, including an inventory of running processes, system performance information, resource utilization, and network dependencies. The information collected by agents is secured at rest and in transit to the Application Discovery Service database in the cloud. AWS Partner Network (APN) solutions integrate with Application Discovery Service, enabling you to import details of your on-premises environment directly into Migration Hub without using the discovery connector or discovery agent. Third-party application discovery tools can query AWS Application Discovery Service, and they can write to the Application Discovery Service database using the public API. In this way, you can import data into Migration Hub and view it, so that you can associate applications with servers and track migrations. Recommendations We recommend that you use agent-based discovery for non-VMware environments, and whenever you want to collect information about network dependencies. You can run agent-based and agentless discovery simultaneously. Use agentless discovery to complete the initial infrastructure assessment quickly, and then install agents on select hosts to collect additional information. Working With This Guide This API reference provides descriptions, syntax, and usage examples for each of the actions and data types for Application Discovery Service. The topic for each action shows the API request parameters and the response. Alternatively, you can use one of the AWS SDKs to access an API that is tailored to the programming language or platform that you're using. For more information, see AWS SDKs. Remember that you must set your Migration Hub home region before you call any of these APIs. You must make API calls for write actions (create, notify, associate, disassociate, import, or put) while in your home region, or a HomeRegionNotSetException error is returned. API calls for read actions (list, describe, stop, and delete) are permitted outside of your home region. Although it is unlikely, the Migration Hub home region could change. If you call APIs outside the home region, an InvalidInputException is returned. You must call GetHomeRegion to obtain the latest Migration Hub home region. This guide is intended for use with the AWS Application Discovery Service User Guide. All data is handled according to the AWS Privacy Policy. You can operate Application Discovery Service offline to inspect collected data before it is shared with the service.

DataSync DataSync is a managed data transfer service that makes it simpler for you to automate moving data between on-premises storage and Amazon Simple Storage Service (Amazon S3) or Amazon Elastic File System (Amazon EFS). This API interface reference for DataSync contains documentation for a programming interface that you can use to manage DataSync.

AWS Fault Injection Simulator is a managed service that enables you to perform fault injection experiments on your AWS workloads. For more information, see the AWS Fault Injection Simulator User Guide.

Welcome to the IoT SiteWise API Reference. IoT SiteWise is an Amazon Web Services service that connects Industrial Internet of Things (IIoT) devices to the power of the Amazon Web Services Cloud. For more information, see the IoT SiteWise User Guide. For information about IoT SiteWise quotas, see Quotas in the IoT SiteWise User Guide.

AWS Global Accelerator This is the AWS Global Accelerator API Reference. This guide is for developers who need detailed information about AWS Global Accelerator API actions, data types, and errors. For more information about Global Accelerator features, see the AWS Global Accelerator Developer Guide. AWS Global Accelerator is a service in which you create accelerators to improve the performance of your applications for local and global users. Depending on the type of accelerator you choose, you can gain additional benefits. By using a standard accelerator, you can improve availability of your internet applications that are used by a global audience. With a standard accelerator, Global Accelerator directs traffic to optimal endpoints over the AWS global network. For other scenarios, you might choose a custom routing accelerator. With a custom routing accelerator, you can use application logic to directly map one or more users to a specific endpoint among many endpoints. Global Accelerator is a global service that supports endpoints in multiple AWS Regions but you must specify the US West (Oregon) Region to create or update accelerators. By default, Global Accelerator provides you with two static IP addresses that you associate with your accelerator. With a standard accelerator, instead of using the IP addresses that Global Accelerator provides, you can configure these entry points to be IPv4 addresses from your own IP address ranges that you bring to Global Accelerator. The static IP addresses are anycast from the AWS edge network. For a standard accelerator, they distribute incoming application traffic across multiple endpoint resources in multiple AWS Regions, which increases the availability of your applications. Endpoints for standard accelerators can be Network Load Balancers, Application Load Balancers, Amazon EC2 instances, or Elastic IP addresses that are located in one AWS Region or multiple Regions. For custom routing accelerators, you map traffic that arrives to the static IP addresses to specific Amazon EC2 servers in endpoints that are virtual private cloud (VPC) subnets. The static IP addresses remain assigned to your accelerator for as long as it exists, even if you disable the accelerator and it no longer accepts or routes traffic. However, when you delete an accelerator, you lose the static IP addresses that are assigned to it, so you can no longer route traffic by using them. You can use IAM policies like tag-based permissions with Global Accelerator to limit the users who have permissions to delete an accelerator. For more information, see Tag-based policies. For standard accelerators, Global Accelerator uses the AWS global network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure. The service reacts instantly to changes in health or configuration to ensure that internet traffic from clients is always directed to healthy endpoints. For a list of the AWS Regions where Global Accelerator and other services are currently supported, see the AWS Region Table. AWS Global Accelerator includes the following components: Static IP addresses Global Accelerator provides you with a set of two static IP addresses that are anycast from the AWS edge network. If you bring your own IP address range to AWS (BYOIP) to use with a standard accelerator, you can instead assign IP addresses from your own pool to use with your accelerator. For more information, see Bring your own IP addresses (BYOIP) in AWS Global Accelerator. The IP addresses serve as single fixed entry points for your clients. If you already have Elastic Load Balancing load balancers, Amazon EC2 instances, or Elastic IP address resources set up for your applications, you can easily add those to a standard accelerator in Global Accelerator. This allows Global Accelerator to use static IP addresses to access the resources. The static IP addresses remain assigned to your accelerator for as long as it exists, even if you disable the accelerator and it no longer accepts or routes traffic. However, when you delete an accelerator, you lose the static IP addresses that are assigned to it, so you can no longer route traffic by using them. You can use IAM policies like tag-based permissions with Global Accelerator to delete an accelerator. For more information, see Tag-based policies. Accelerator An accelerator directs traffic to endpoints over the AWS global network to improve the performance of your internet applications. Each accelerator includes one or more listeners. There are two types of accelerators: A standard accelerator directs traffic to the optimal AWS endpoint based on several factors, including the user’s location, the health of the endpoint, and the endpoint weights that you configure. This improves the availability and performance of your applications. Endpoints can be Network Load Balancers, Application Load Balancers, Amazon EC2 instances, or Elastic IP addresses. A custom routing accelerator directs traffic to one of possibly thousands of Amazon EC2 instances running in a single or multiple virtual private clouds (VPCs). With custom routing, listener ports are mapped to statically associate port ranges with VPC subnets, which allows Global Accelerator to determine an EC2 instance IP address at the time of connection. By default, all port mapping destinations in a VPC subnet can't receive traffic. You can choose to configure all destinations in the subnet to receive traffic, or to specify individual port mappings that can receive traffic. For more information, see Types of accelerators. DNS name Global Accelerator assigns each accelerator a default Domain Name System (DNS) name, similar to a1234567890abcdef.awsglobalaccelerator.com, that points to the static IP addresses that Global Accelerator assigns to you or that you choose from your own IP address range. Depending on the use case, you can use your accelerator's static IP addresses or DNS name to route traffic to your accelerator, or set up DNS records to route traffic using your own custom domain name. Network zone A network zone services the static IP addresses for your accelerator from a unique IP subnet. Similar to an AWS Availability Zone, a network zone is an isolated unit with its own set of physical infrastructure. When you configure an accelerator, by default, Global Accelerator allocates two IPv4 addresses for it. If one IP address from a network zone becomes unavailable due to IP address blocking by certain client networks, or network disruptions, then client applications can retry on the healthy static IP address from the other isolated network zone. Listener A listener processes inbound connections from clients to Global Accelerator, based on the port (or port range) and protocol (or protocols) that you configure. A listener can be configured for TCP, UDP, or both TCP and UDP protocols. Each listener has one or more endpoint groups associated with it, and traffic is forwarded to endpoints in one of the groups. You associate endpoint groups with listeners by specifying the Regions that you want to distribute traffic to. With a standard accelerator, traffic is distributed to optimal endpoints within the endpoint groups associated with a listener. Endpoint group Each endpoint group is associated with a specific AWS Region. Endpoint groups include one or more endpoints in the Region. With a standard accelerator, you can increase or reduce the percentage of traffic that would be otherwise directed to an endpoint group by adjusting a setting called a traffic dial. The traffic dial lets you easily do performance testing or blue/green deployment testing, for example, for new releases across different AWS Regions. Endpoint An endpoint is a resource that Global Accelerator directs traffic to. Endpoints for standard accelerators can be Network Load Balancers, Application Load Balancers, Amazon EC2 instances, or Elastic IP addresses. An Application Load Balancer endpoint can be internet-facing or internal. Traffic for standard accelerators is routed to endpoints based on the health of the endpoint along with configuration options that you choose, such as endpoint weights. For each endpoint, you can configure weights, which are numbers that you can use to specify the proportion of traffic to route to each one. This can be useful, for example, to do performance testing within a Region. Endpoints for custom routing accelerators are virtual private cloud (VPC) subnets with one or many EC2 instances.

Amazon Elastic Container Service Amazon Elastic Container Service (Amazon ECS) is a highly scalable, fast, container management service that makes it easy to run, stop, and manage Docker containers on a cluster. You can host your cluster on a serverless infrastructure that is managed by Amazon ECS by launching your services or tasks on Fargate. For more control, you can host your tasks on a cluster of Amazon Elastic Compute Cloud (Amazon EC2) instances that you manage. Amazon ECS makes it easy to launch and stop container-based applications with simple API calls, allows you to get the state of your cluster from a centralized service, and gives you access to many familiar Amazon EC2 features. You can use Amazon ECS to schedule the placement of containers across your cluster based on your resource needs, isolation policies, and availability requirements. Amazon ECS eliminates the need for you to operate your own cluster management and configuration management systems or worry about scaling your management infrastructure.

Welcome to the AWS Device Farm API documentation, which contains APIs for: Testing on desktop browsers Device Farm makes it possible for you to test your web applications on desktop browsers using Selenium. The APIs for desktop browser testing contain TestGrid in their names. For more information, see Testing Web Applications on Selenium with Device Farm. Testing on real mobile devices Device Farm makes it possible for you to test apps on physical phones, tablets, and other devices in the cloud. For more information, see the Device Farm Developer Guide.

The Azure SQL Database management API provides a RESTful set of web APIs that interact with Azure SQL Database services to manage your databases. The API enables users to create, retrieve, update, and delete databases, servers, and other entities.

Amazon Personalize can consume real-time user event data, such as stream or click data, and use it for model training either alone or combined with historical data. For more information see Recording Events.

Amazon Augmented AI (Amazon A2I) adds the benefit of human judgment to any machine learning application. When an AI application can't evaluate data with a high degree of confidence, human reviewers can take over. This human review is called a human review workflow. To create and start a human review workflow, you need three resources: a worker task template, a flow definition, and a human loop. For information about these resources and prerequisites for using Amazon A2I, see Get Started with Amazon Augmented AI in the Amazon SageMaker Developer Guide. This API reference includes information about API actions and data types that you can use to interact with Amazon A2I programmatically. Use this guide to: Start a human loop with the StartHumanLoop operation when using Amazon A2I with a custom task type. To learn more about the difference between custom and built-in task types, see Use Task Types. To learn how to start a human loop using this API, see Create and Start a Human Loop for a Custom Task Type in the Amazon SageMaker Developer Guide. Manage your human loops. You can list all human loops that you have created, describe individual human loops, and stop and delete human loops. To learn more, see Monitor and Manage Your Human Loop in the Amazon SageMaker Developer Guide. Amazon A2I integrates APIs from various AWS services to create and start human review workflows for those services. To learn how Amazon A2I uses these APIs, see Use APIs in Amazon A2I in the Amazon SageMaker Developer Guide.

AWS Auto Scaling Use AWS Auto Scaling to create scaling plans for your applications to automatically scale your scalable AWS resources. API Summary You can use the AWS Auto Scaling service API to accomplish the following tasks: Create and manage scaling plans Define target tracking scaling policies to dynamically scale your resources based on utilization Scale Amazon EC2 Auto Scaling groups using predictive scaling and dynamic scaling to scale your Amazon EC2 capacity faster Set minimum and maximum capacity limits Retrieve information on existing scaling plans Access current forecast data and historical forecast data for up to 56 days previous To learn more about AWS Auto Scaling, including information about granting IAM users required permissions for AWS Auto Scaling actions, see the AWS Auto Scaling User Guide.

Resource health operation endpoints and objects.

Time Series Insights environment data plane client for PAYG (Preview L1 SKU) environments.

API spec for Microsoft.SecurityInsights (Azure Security Insights) resource provider

With Application Auto Scaling, you can configure automatic scaling for the following resources: Amazon AppStream 2.0 fleets Amazon Aurora Replicas Amazon Comprehend document classification and entity recognizer endpoints Amazon DynamoDB tables and global secondary indexes throughput capacity Amazon ECS services Amazon ElastiCache for Redis clusters (replication groups) Amazon EMR clusters Amazon Keyspaces (for Apache Cassandra) tables Lambda function provisioned concurrency Amazon Managed Streaming for Apache Kafka broker storage Amazon SageMaker endpoint variants Spot Fleet (Amazon EC2) requests Custom resources provided by your own applications or services API Summary The Application Auto Scaling service API includes three key sets of actions: Register and manage scalable targets - Register Amazon Web Services or custom resources as scalable targets (a resource that Application Auto Scaling can scale), set minimum and maximum capacity limits, and retrieve information on existing scalable targets. Configure and manage automatic scaling - Define scaling policies to dynamically scale your resources in response to CloudWatch alarms, schedule one-time or recurring scaling actions, and retrieve your recent scaling activity history. Suspend and resume scaling - Temporarily suspend and later resume automatic scaling by calling the RegisterScalableTarget API action for any Application Auto Scaling scalable target. You can suspend and resume (individually or in combination) scale-out activities that are triggered by a scaling policy, scale-in activities that are triggered by a scaling policy, and scheduled scaling. To learn more about Application Auto Scaling, including information about granting IAM users required permissions for Application Auto Scaling actions, see the Application Auto Scaling User Guide.

AWS Marketplace Metering Service This reference provides descriptions of the low-level AWS Marketplace Metering Service API. AWS Marketplace sellers can use this API to submit usage data for custom usage dimensions. For information on the permissions you need to use this API, see AWS Marketing metering and entitlement API permissions in the AWS Marketplace Seller Guide. Submitting Metering Records MeterUsage - Submits the metering record for a Marketplace product. MeterUsage is called from an EC2 instance or a container running on EKS or ECS. BatchMeterUsage - Submits the metering record for a set of customers. BatchMeterUsage is called from a software-as-a-service (SaaS) application. Accepting New Customers ResolveCustomer - Called by a SaaS application during the registration process. When a buyer visits your website during the registration process, the buyer submits a Registration Token through the browser. The Registration Token is resolved through this API to obtain a CustomerIdentifier and Product Code. Entitlement and Metering for Paid Container Products Paid container software products sold through AWS Marketplace must integrate with the AWS Marketplace Metering Service and call the RegisterUsage operation for software entitlement and metering. Free and BYOL products for Amazon ECS or Amazon EKS aren't required to call RegisterUsage, but you can do so if you want to receive usage data in your seller reports. For more information on using the RegisterUsage operation, see Container-Based Products. BatchMeterUsage API calls are captured by AWS CloudTrail. You can use Cloudtrail to verify that the SaaS metering records that you sent are accurate by searching for records with the eventName of BatchMeterUsage. You can also use CloudTrail to audit records over time. For more information, see the AWS CloudTrail User Guide .

API spec for Microsoft.Security (Azure Security Center) resource provider

Alert operation endpoints and objects.